Caricatures are facial drawings by artists with exaggeration on certain facial parts. The exaggerations are often beyond realism and yet the caricatures are still recognizable by humans. With the advent of deep learning, recognition performances by computers on real-world faces has become comparable to human performance even under unconstrained situations. However, there is still a gap in caricature recognition performance between computer and human. This is mainly due to the lack of publicly available caricature datasets of large scale. To facilitate the research in caricature recognition, a new caricature dataset is built. All the caricature images and face images were collected from the web.Compared with two existing datasets, this dataset is of larger size and has various artistic styles. We also offer evaluation protocols and present baseline performances on the dataset. Specifically, four evaluation protocols are provided: restricted and unrestricted caricature verifications, caricature to photo and photo to caricature face identifications. Based on the evaluation protocols, three face alignment methods together with five kinds of features and nine subspace and metric learning algorithms have been applied to provide the baseline performances on this dataset. Main conclusion is that there is still a space for improvement in caricature face recognition.

本文建立了一个新的漫画数据集，旨在促进漫画识别的研究。该数据集比现有的两个数据集更具挑战性，在可用图像、艺术风格和个人间变化方面更大。此外，还提出了一个漫画人脸识别框架，用于分析漫画识别的挑战和算法的表现。结论是还有很大的提升空间，并且分析的问题仍需要进一步研究。

WebCaricature: 夸张画像识别基准